Competing time-to-event endpoints in cardiology trials: A simulation study to illustrate the importance of an adequate statistical analysis

Abstract

Clinical trials in cardiology commonly consider time-to-event endpoints that are often influenced by competing risks. In the presence of competing risks, standard survival analysis techniques, such as the Kaplan-Meier estimator, can yield seriously biased results. Although methods to account for competing risks are well known in the statistical literature, they are rarely applied in clinical trials. Simulation study, to demonstrate the appropriate application and interpretation of the competing risks methodology with respect to time-to-event endpoints. In this paper, different statistical approaches to account for competing risks are systematically compared, based on a simulation study and using the original data from a cardiology trial. Group comparisons in clinical trials that have competing time-to-event endpoints should be based on the cause-specific hazard functions. In contrast, group comparisons based on event rates should be carried out with care, as event rates are directly influenced by competing events. Ignoring or not fully accounting for competing risks may yield misleading or even erroneous results, which could hinder understanding of survival trends; therefore, it is important that competing risks methodology be routinely incorporated into clinical trial standards.